SoroushVahidi/scidocs huggingface dataset:
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization
(2012)

Can one parallelize complex exploration– exploitation tradeoffs? As an example, consider the problem of optimal highthroughput experimental design, where we wish to sequentially design batches of experiments in order to simultaneously learn a surrogate function mapping stimulus to response and identify the maximum of the function. We formalize the task as a multiarmed bandit problem, where the unknown payoff function is sampled from a Gaussian process (GP), and instead of a single arm, in each round we pull a batch of several arms in parallel. We develop GP-BUCB, a principled algorithm for choosing batches, based on the GP-UCB algorithm for sequential GP optimization. We prove a surprising result; as compared to the sequential approach, the cumulative regret of the parallel algorithm only increases by a constant factor independent of the batch size B. Our results provide rigorous theoretical support for exploiting parallelism in Bayesian global optimization. We demonstrate the effectiveness of our approach on two real-world applications.
Using Computer Programming to Enhance Science Learning for 5th Graders in Taipei
(2012)

The purpose of this study is to evaluate the effectiveness of using Scratch Programming in science learning for 5th graders in Taipei. A quasi-experimental design with a single group was used in this study. 96 fifth graders at an elementary school in Taipei participated in 15 weeks of Scratch Programming activities to enrich computer learning during their science learning. Three research instruments were used in this study, including a logical thinking test, a problem solving test, and a learning questionnaire. The results were subjected to t-test and frequency analysis. The findings of this study include (1) the outcomes of the logical thinking test and the problem solving test indicate that students had better performance in logical thinking and problem solving, (2) feedback from the study questionnaire indicates that more than 71.4% of students favored using Scratch Programming in their computer learning, and more than 58.4% of students favored designing Scratch Programming in their science learning, (3) more than 54.7% of students reported that they are willing to do more similar computer programming projects for other science units in the future. In view of the above findings, this study concludes that Scratch Programming is effective for 5th graders in Taipei.
New E-Commerce User Interest Patterns
(2017)

The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.
S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES
(2016)

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “ ||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.
A requirements monitoring framework for enterprise systems
(2005)

Requirements compliant software is becoming a necessity. Fewer and fewer organizations will run their critical transactions on software that has no visible relationship to its requirements. Businesses wish to see their software being consistent with their policies. Moreover, partnership agreements are pressuring less mature organizations to improve their systems. Businesses that rely on web services, for example, are vulnerable to the problems of their web service providers. While electronic commerce has increased the speed of on-line transactions, the technology for monitoring requirements compliance—especially for transactions—has lagged behind. To address the requirements monitoring problem for enterprise information systems, we integrate techniques for requirements analysis and software execution monitoring. Our framework assists analysts in the development of requirements monitors for enterprise services. The deployed system raises alerts when services succeed or fail to satisfy their specified requirements, thereby making software requirements visible. The framework usage is demonstrated with an analysis of ebXML marketplace specifications. An analyst applies goal analysis to discover potential service obstacles, and then derives requirements monitors and a distributed monitoring system. Once deployed, the monitoring system provides alerts when obstacles occur. A summary of the framework implementation is presented, along with analysis of two monitor component implementations. We conclude that the approach implemented in the framework, called ReqMon, provides real-time feedback on requirements satisfaction, and thereby provides visibility into requirements compliance of enterprise information systems.
Differential Dataflow
(2013)

Existing computational models for processing continuously changing input data are unable to efficiently support iterative queries except in limited special cases. This makes it difficult to perform complex tasks, such as social-graph analysis on changing data at interactive timescales, which would greatly benefit those analyzing the behavior of services like Twitter. In this paper we introduce a new model called differential computation, which extends traditional incremental computation to allow arbitrarily nested iteration, and explain—with reference to a publicly available prototype system called Naiad—how differential computation can be efficiently implemented in the context of a declarative dataparallel dataflow language. The resulting system makes it easy to program previously intractable algorithms such as incrementally updated strongly connected components, and integrate them with data transformation operations to obtain practically relevant insights from real data streams.
ioCane: A Smart-Phone and Sensor-Augmented Mobility Aid for the Blind
(2013)

We present the design, implementation, and early results of ioCane, a mobility aid for blind cane users that uses detachable cane-mounted ultrasonic sensors connected to a circuit board to send contextual data wirelessly to an Android phone application. The system uses the built-in mobile phone modalities of vibrations and chimes to alert the user to object height and proximity. We believe this plug-and-play solution for visually impaired users has the potential to enhance user mobility and object avoidance with a minimal learning curve. A pilot study testing the performance of the ioCane with blind cane users showed a 47.3% improvement in obstacle avoidance. To our knowledge, the ioCane is the first sensor-based mobility assistance system to integrate natively with a mobile phone without any modifications to the phone or the system, as well as the first system of its kind to be evaluated by actual visually-impaired cane users. ACM Classification
"Soft" Exoskeletons for Upper and Lower Body Rehabilitation - Design, Control and Testing
(2007)

The basic concepts for exoskeletal systems have been suggested for some time with applications ranging from construction, manufacturing and mining to rescue and emergency services. In recent years, research has been driven by possible uses in medical/rehabilitation and military applications. Yet there are still significant barriers to the effective use and exploitation of this technology. Among the most pertinent of these factors is the power and actuation system and its impact of control, strength, speed and, perhaps most critically, safety. This work describes the design, construction and testing of an ultra low-mass, full-body exoskeleton system having seven degrees of freedom (DOFs) for the upper limbs and five degrees of freedom (DOFs) for each of the lower limbs. This low mass is primarily due to the use of a new range of pneumatic muscle actuators as the power source for the system. The work presented will show how the system takes advantage of the inherent controllable compliance to produce a unit that is powerful, providing a wide range of functionality (motion and forces over an extended range) in a manner that has high safety integrity for the user. The general layout of both the upper and the lower body exoskeleton is presented together with results from preliminary experiments to demonstrate the potential of the device in limb retraining, rehabilitation and power assist (augmentation) operations.
Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation
(2018)

In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation. Using a fully-convolutional backbone architecture, we obtain volumetric heatmaps per body joint, which we convert to coordinates using soft-argmax. Absolute person center depth is estimated by a 1D heatmap prediction head. The coordinates are back-projected to 3D camera space, where we minimize the L1 loss. Key to our good results is the training data augmentation with randomly placed occluders from the Pascal VOC dataset. In addition to reaching first place in the Challenge, our method also surpasses the state-of-the-art on the full Human3.6M benchmark when considering methods that use no extra pose datasets in training. Code for applying synthetic occlusions is availabe at https://github.com/ isarandi/synthetic-occlusion.
The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea
(2013)

We performed a study to determine the influence that site quality has on repurchase intention of Internet shopping through customer satisfaction, customer trust, and customer commitment. Appropriate measures were developed and tested on 230 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The results of the empirical analysis confirmed that site quality can be conceptualized as a composite of six dimensions of shopping convenience, site design, information usefulness, transaction security, payment system, and customer communication. Second, site quality positively affected customer satisfaction and customer trust, but did not affect customer commitment and repurchase intention. Third, site quality can affect repurchase intention by enhancing or attenuating customer satisfaction, customer trust, and customer commitment in online transaction situation. The mediating effect of customer satisfaction, customer trust, and customer commitment between site quality and repurchase intention is identified. Fourth, site quality indirectly affected customer commitment through customer satisfaction. Customer satisfaction indirectly affected repurchase intention through customer trust and customer commitment. Thus, it is found that site quality can be a very important factor to enhance repurchase intention in the customer perspective. © 2013 Elsevier Ltd. All rights reserved.
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
(2017)

Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec—two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple k-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.
QUASY: Quantitative Synthesis Tool
(2011)

We present the tool Q UASY, a quantitative synthesis tool. Q UASY takes qualitative and quantitative specifications and automatic ally onstructs a system that satisfies the qualitative specification and optimizes the qu antitative specification, if such a system exists. The user can choose between a system that satisfies and optimi zes the specifications (a) under all possible environment behaviors or (b) under th most-likely environment behaviors given as a probability distribution on the po ssible input sequences. QUASY solves these two quantitative synthesis problems by reduct ion to instances of 2-player games and Markov Decision Processes (MDPs) with qua ntitative winning objectives. QUASY can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with meanpayoff objectives, and ergodic MDPs with mean-payoff parit y objectives.
Gist: A Solver for Probabilistic Games
(2010)

Gist is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides the first and efficient implementations of several reduction-based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications.
Playing Stochastic Games Precisely
(2012)

We study stochastic two-player games where the goal of one player is to achieve precisely a given expected value of the objective function, while the goal of the opponent is the opposite. Potential applications for such games include controller synthesis problems where the optimisation objective is to maximise or minimise a given payoff function while respecting a strict upper or lower bound, respectively. We consider a number of objective functions including reachability, ω-regular, discounted reward, and total reward. We show that precise value games are not determined, and compare the memory requirements for winning strategies. For stopping games we establish necessary and sufficient conditions for the existence of a winning strategy of the controller for a large class of functions, as well as provide the constructions of compact strategies for the studied objectives.
PRISM 4.0: Verification of Probabilistic Real-Time Systems
(2011)

This paper describes a major new release of the PRISM probabilistic model checker, adding, in particular, quantitative verification of (priced) probabilistic timed automata. These model systems exhibiting probabilistic, nondeterministic and real-time characteristics. In many application domains, all three aspects are essential; this includes, for example, embedded controllers in automotive or avionic systems, wireless communication protocols such as Bluetooth or Zigbee, and randomised security protocols. PRISM, which is open-source, also contains several new components that are of independent use. These include: an extensible toolkit for building, verifying and refining abstractions of probabilistic models; an explicit-state probabilistic model checking library; a discrete-event simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite.
The Ins and Outs of the Probabilistic Model Checker MRMC
(2009)

The Markov Reward Model Checker (MRMC) is a software tool for verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their reward extensions. Distinguishing features of MRMC are its support for computing timeand reward-bounded reachability probabilities, (property-driven) bisimulation minimization, and precise on-the-fly steady-state detection. Recent tool features include time-bounded reachability analysis for uniform CTMDPs and CSL model checking by discrete-event simulation. This paper presents the tool’s current status and its implementation details.
Connectivity in a citation network: The development of DNA theory
(1989)

The study of citation networks for both articles and journals is routine. In general, these analyses proceed by considering the similarity of articles or journals and submitting the set of similarity measures to some clustering or scaling procedure. Two common methods are found in bibliomettic coupling, where two citing articles are similar to the extent they cite the same literature, and co-citation analysis where cited articles are similar to the extent they are cited by the same citing articles. Methods based on structural and regular equivalence also seek to partition the article based on their positional location. Such methods have in common focus on the articles and partitions of them. We propose a quite different approach where the connective threads through a network are preserved and the focus is on the links in the network rather than on the nodes. Variants of the depth first search algorithm are used to detect and represent the mainstream of the literature of a clearly delineated area of scientific research. The specific citation network is one that consists of ties among the key events and papers that lead to the discovery and modeling of DNA together with the final experimental confirmation of its representation.
Semantic similarity based evaluation for C programs through the use of symbolic execution
(2016)

Automatic grading of programs has existed in various fields for many years ago. Within this paper, we propose a method for evaluating C programs. Two approaches are distinguished in this context: static and dynamic analysis methods. Unlike the dynamic analysis that requires an executable program to be evaluated, static analysis could evaluate a program even if it is not totally correct. The proposed method is based on static analysis of programs. It consists of comparing the evaluated program with the evaluator-provided program through their Control Flow Graphs. Here, the great challenge is to deal with the multiplicity of solutions that exists for the same programming problem. As a solution to this weakness, we propose an innovative similarity measure that compares two programs according to their semantic executions. In fact, the evaluated program is compared to the evaluator-provided program called model program by using the symbolic execution technique. The experimentations presented in this work are performed by using a basic implementation of the proposed method. The obtained results reveal a promising realization in the field of automated evaluation of programs. They also show that the proposed method guarantees a considerable approximation to the human program evaluation.
Topic Models for Image Annotation and Text Illustration
(2010)

Image annotation, the task of automatically generating description words for a picture, is a key component in various image search and retrieval applications. Creating image databases for model development is, however, costly and time consuming, since the keywords must be hand-coded and the process repeated for new collections. In this work we exploit the vast resource of images and documents available on the web for developing image annotation models without any human involvement. We describe a probabilistic model based on the assumption that images and their co-occurring textual data are generated by mixtures of latent topics. We show that this model outperforms previously proposed approaches when applied to image annotation and the related task of text illustration despite the noisy nature of our dataset.
Home telehealth - Current state and future trends
(2006)

OBJECTIVE
The purpose of this paper is to give an overview about the state of the art in research on home telehealth in an international perspective.


METHOD
The study is based on a review of the scientific literature published between 1990 and 2003 and retrieved via Medline in January/February 2004. All together, the abstracts of 578 publications have been analyzed.


RESULTS
The majority of publications (44%) comes from the United States, followed by UK and Japan. Most publications deal with vital sign parameter (VSP) measurement and audio/video consultations ("virtual visits"). Publications about IT tools for improved information access and communication as well as decision support for staff, patients and relatives are relatively sparse. Clinical application domains are mainly chronic diseases, the elderly population and paediatrics.


CONCLUSIONS
Internationally, we observe a trend towards tools and services not only for professionals but also for patients and citizens. However, their impact on the patient-provider relationship and their design for special user groups, such as elderly and/or disabled needs to be further explored. In general, evaluation studies are rare and further research is critical to determine the impacts and benefits, and limitations, of potential solutions and to overcome a number of hinders and restrictions, such as - the lack of standards to combine incompatible information systems; - the lack of an evaluation framework considering legal, ethical, organisational, clinical, usability and technical aspects; - the lack of proper guidelines for practical implementation of home telehealth solutions.